公开(公告)号：US08892441B2

公开(公告)日：2014-11-18

申请号：US13311512

申请日：2011-12-05

申请人： Alistair D. Conkie ,  Mazin Gilbert ,  Andrej Ljolje

发明人： Alistair D. Conkie ,  Mazin Gilbert ,  Andrej Ljolje

IPC分类号： G10L15/187 ,  G10L15/06

CPC分类号： G06F17/277 ,  G10L15/063 ,  G10L15/187

摘要： The present disclosure relates to systems, methods, and computer-readable media for generating a lexicon for use with speech recognition. The method includes overgenerating potential pronunciations based on symbolic input, identifying potential pronunciations in a speech recognition context, and storing the identified potential pronunciations in a lexicon. Overgenerating potential pronunciations can include establishing a set of conversion rules for short sequences of letters, converting portions of the symbolic input into a number of possible lexical pronunciation variants based on the set of conversion rules, modeling the possible lexical pronunciation variants in one of a weighted network and a list of phoneme lists, and iteratively retraining the set of conversion rules based on improved pronunciations. Symbolic input can include multiple examples of a same spoken word. Speech data can be labeled explicitly or implicitly and can include words as text and recorded audio.

摘要翻译： 本公开涉及用于生成用于语音识别的词典的系统，方法和计算机可读介质。 该方法包括基于符号输入过度生成潜在发音，识别语音识别语境中的潜在发音，以及将识别的潜在发音存储在词典中。 过度生成潜在发音可以包括为短的字母序列建立一组转换规则，基于一组转换规则将符号输入的部分转换成许多可能的词汇发音变体，对可能的词汇发音变体在加权 网络和音素列表，并且基于改进的发音迭代地重新训练一组转换规则。 符号输入可以包括相同口语单词的多个示例。 语音数据可以被明确地或隐含地标记，并且可以将单词包括为文本和记录的音频。

公开(公告)号：US20100125457A1

公开(公告)日：2010-05-20

申请号：US12274025

申请日：2008-11-19

申请人： Mazin Gilbert ,  Alistair D. Conkie ,  Andrej Ljolje

发明人： Mazin Gilbert ,  Alistair D. Conkie ,  Andrej Ljolje

IPC分类号： G10L15/04

CPC分类号： G10L15/063 ,  G10L2015/025

摘要： Disclosed herein are systems, computer-implemented methods, and computer-readable media for speech recognition. The method includes receiving speech utterances, assigning a pronunciation weight to each unit of speech in the speech utterances, each respective pronunciation weight being normalized at a unit of speech level to sum to 1, for each received speech utterance, optimizing the pronunciation weight by (1) identifying word and phone alignments and corresponding likelihood scores, and (2) discriminatively adapting the pronunciation weight to minimize classification errors, and recognizing additional received speech utterances using the optimized pronunciation weights. A unit of speech can be a sentence, a word, a context-dependent phone, a context-independent phone, or a syllable. The method can further include discriminatively adapting pronunciation weights based on an objective function. The objective function can be maximum mutual information (MMI), maximum likelihood (MLE) training, minimum classification error (MCE) training, or other functions known to those of skill in the art. Speech utterances can be names. The speech utterances can be received as part of a multimodal search or input. The step of discriminatively adapting pronunciation weights can further include stochastically modeling pronunciations.

摘要翻译： 本文公开了用于语音识别的系统，计算机实现的方法和计算机可读介质。 该方法包括接收语音话语，在语音话语中为每个语音单元分配发音权重，将每个相应的发音权重以语音级别为单位归一化为1，对于每个接收到的语音话语，通过（ 1）识别词和电话对齐和相应的可能性分数，以及（2）歧视地调整发音权重以最小化分类错误，以及使用优化的发音权重来识别附加的接收到的语音话语。 语音单位可以是句子，单词，上下文相关的电话，与上下文无关的电话或音节。 该方法还可以包括基于目标函数的歧视地适应发音权重。 目标函数可以是本领域技术人员已知的最大相互信息（MMI），最大似然（MLE）训练，最小分类误差（MCE）训练或其他功能。 言语言可以是名字。 可以作为多模态搜索或输入的一部分接收演讲话语。 歧视性地适应发音权重的步骤还可以包括随机建模发音。

公开(公告)号：US08296141B2

公开(公告)日：2012-10-23

申请号：US12274025

申请日：2008-11-19

申请人： Mazin Gilbert ,  Alistair D. Conkie ,  Andrej Ljolje

发明人： Mazin Gilbert ,  Alistair D. Conkie ,  Andrej Ljolje

IPC分类号： G10L15/04

CPC分类号： G10L15/063 ,  G10L2015/025

摘要： Disclosed herein are systems, computer-implemented methods, and computer-readable media for speech recognition. The method includes receiving speech utterances, assigning a pronunciation weight to each unit of speech in the speech utterances, each respective pronunciation weight being normalized at a unit of speech level to sum to 1, for each received speech utterance, optimizing the pronunciation weight by (1) identifying word and phone alignments and corresponding likelihood scores, and (2) discriminatively adapting the pronunciation weight to minimize classification errors, and recognizing additional received speech utterances using the optimized pronunciation weights. A unit of speech can be a sentence, a word, a context-dependent phone, a context-independent phone, or a syllable. The method can further include discriminatively adapting pronunciation weights based on an objective function. The objective function can be maximum mutual information (MMI), maximum likelihood (MLE) training, minimum classification error (MCE) training, or other functions known to those of skill in the art. Speech utterances can be names. The speech utterances can be received as part of a multimodal search or input. The step of discriminatively adapting pronunciation weights can further include stochastically modeling pronunciations.

摘要翻译： 本文公开了用于语音识别的系统，计算机实现的方法和计算机可读介质。 该方法包括接收语音话语，在语音话语中为每个语音单元分配发音权重，将每个相应的发音权重以语音级别为单位归一化为1，对于每个接收到的语音话语，通过（ 1）识别词和电话对齐和相应的可能性分数，以及（2）歧视地调整发音权重以最小化分类错误，以及使用优化的发音权重来识别附加的接收到的语音话语。 语音单位可以是句子，单词，上下文相关的电话，与上下文无关的电话或音节。 该方法还可以包括基于目标函数的歧视地适应发音权重。 目标函数可以是本领域技术人员已知的最大相互信息（MMI），最大似然（MLE）训练，最小分类误差（MCE）训练或其他功能。 言语言可以是名字。 可以作为多模态搜索或输入的一部分接收演讲话语。 歧视性地适应发音权重的步骤还可以包括随机建模发音。

公开(公告)号：US20120078617A1

公开(公告)日：2012-03-29

申请号：US13311512

申请日：2011-12-05

申请人： Alistair D. Conkie ,  Mazin Gilbert ,  Andrej Ljolje

发明人： Alistair D. Conkie ,  Mazin Gilbert ,  Andrej Ljolje

IPC分类号： G06F17/21

CPC分类号： G06F17/277 ,  G10L15/063 ,  G10L15/187

摘要： The present disclosure relates to systems, methods, and computer-readable media for generating a lexicon for use with speech recognition. The method includes receiving symbolic input as labeled speech data, overgenerating potential pronunciations based on the symbolic input, identifying potential pronunciations in a speech recognition context, and storing the identified potential pronunciations in a lexicon. Overgenerating potential pronunciations can include establishing a set of conversion rules for short sequences of letters, converting portions of the symbolic input into a number of possible lexical pronunciation variants based on the set of conversion rules, modeling the possible lexical pronunciation variants in one of a weighted network and a list of phoneme lists, and iteratively retraining the set of conversion rules based on improved pronunciations. Symbolic input can include multiple examples of a same spoken word. Speech data can be labeled explicitly or implicitly and can include words as text and recorded audio.

摘要翻译： 本公开涉及用于生成用于语音识别的词典的系统，方法和计算机可读介质。 所述方法包括：将符号输入作为标记的语音数据接收，基于所述符号输入过度生成潜在发音，识别语音识别语境中的潜在发音，以及将所识别的潜在发音存储在词典中。 过度生成潜在发音可以包括为短的字母序列建立一组转换规则，基于一组转换规则将符号输入的部分转换成许多可能的词汇发音变体，对可能的词汇发音变体在加权 网络和音素列表，并且基于改进的发音迭代地重新训练一组转换规则。 符号输入可以包括相同口语单词的多个示例。 语音数据可以被明确地或隐含地标记，并且可以将单词包括为文本和记录的音频。

公开(公告)号：US08095365B2

公开(公告)日：2012-01-10

申请号：US12328436

申请日：2008-12-04

申请人： Alistair D. Conkie ,  Mazin Gilbert ,  Andrej Ljolje

发明人： Alistair D. Conkie ,  Mazin Gilbert ,  Andrej Ljolje

IPC分类号： G10L13/08

CPC分类号： G06F17/277 ,  G10L15/063 ,  G10L15/187

摘要： The present disclosure relates to systems, methods, and computer-readable media for generating a lexicon for use with speech recognition. The method includes receiving symbolic input as labeled speech data, overgenerating potential pronunciations based on the symbolic input, identifying potential pronunciations in a speech recognition context, and storing the identified potential pronunciations in a lexicon. Overgenerating potential pronunciations can include establishing a set of conversion rules for short sequences of letters, converting portions of the symbolic input into a number of possible lexical pronunciation variants based on the set of conversion rules, modeling the possible lexical pronunciation variants in one of a weighted network and a list of phoneme lists, and iteratively retraining the set of conversion rules based on improved pronunciations. Symbolic input can include multiple examples of a same spoken word. Speech data can be labeled explicitly or implicitly and can include words as text and recorded audio.

摘要翻译： 本公开涉及用于生成用于语音识别的词典的系统，方法和计算机可读介质。 所述方法包括：将符号输入作为标记的语音数据接收，基于所述符号输入过度生成潜在发音，识别语音识别语境中的潜在发音，以及将所识别的潜在发音存储在词典中。 过度生成潜在发音可以包括为短的字母序列建立一组转换规则，基于一组转换规则将符号输入的部分转换成许多可能的词汇发音变体，对可能的词汇发音变体在加权 网络和音素列表，并且基于改进的发音迭代地重新训练一组转换规则。 符号输入可以包括相同口语单词的多个示例。 语音数据可以被明确地或隐含地标记，并且可以将单词包括为文本和记录的音频。

公开(公告)号：US08548807B2

公开(公告)日：2013-10-01

申请号：US12480848

申请日：2009-06-09

申请人： Andrej Ljolje ,  Alistair D. Conkie ,  Ann K. Syrdal

发明人： Andrej Ljolje ,  Alistair D. Conkie ,  Ann K. Syrdal

IPC分类号： G10L15/04

CPC分类号： G10L17/14 ,  G10L15/063 ,  G10L15/07 ,  G10L15/14 ,  G10L15/187 ,  G10L15/265 ,  G10L15/30 ,  G10L2015/025

摘要： Disclosed herein are systems, computer-implemented methods, and computer-readable storage media for recognizing speech by adapting automatic speech recognition pronunciation by acoustic model restructuring. The method identifies an acoustic model and a matching pronouncing dictionary trained on typical native speech in a target dialect. The method collects speech from a new speaker resulting in collected speech and transcribes the collected speech to generate a lattice of plausible phonemes. Then the method creates a custom speech model for representing each phoneme used in the pronouncing dictionary by a weighted sum of acoustic models for all the plausible phonemes, wherein the pronouncing dictionary does not change, but the model of the acoustic space for each phoneme in the dictionary becomes a weighted sum of the acoustic models of phonemes of the typical native speech. Finally the method includes recognizing via a processor additional speech from the target speaker using the custom speech model.

公开(公告)号：US09002713B2

公开(公告)日：2015-04-07

申请号：US12480864

申请日：2009-06-09

申请人： Andrej Ljolje ,  Alistair D. Conkie ,  Ann K. Syrdal

发明人： Andrej Ljolje ,  Alistair D. Conkie ,  Ann K. Syrdal

IPC分类号： G10L15/00 ,  G10L25/00 ,  G10L21/00 ,  G10L15/07 ,  G10L15/26 ,  G10L15/10

CPC分类号： G10L15/07 ,  G10L15/10 ,  G10L15/265

摘要： Disclosed herein are systems, computer-implemented methods, and tangible computer-readable storage media for speaker recognition personalization. The method recognizes speech received from a speaker interacting with a speech interface using a set of allocated resources, the set of allocated resources including bandwidth, processor time, memory, and storage. The method records metrics associated with the recognized speech, and after recording the metrics, modifies at least one of the allocated resources in the set of allocated resources commensurate with the recorded metrics. The method recognizes additional speech from the speaker using the modified set of allocated resources. Metrics can include a speech recognition confidence score, processing speed, dialog behavior, requests for repeats, negative responses to confirmations, and task completions. The method can further store a speaker personalization profile having information for the modified set of allocated resources and recognize speech associated with the speaker based on the speaker personalization profile.

摘要翻译： 这里公开了用于说话人识别个性化的系统，计算机实现的方法和有形的计算机可读存储介质。 该方法使用一组分配的资源来识别从与语音接口交互的扬声器接收的语音，所分配的资源的集合包括带宽，处理器时间，存储器和存储。 该方法记录与识别的语音相关联的度量，并且在记录度量之后，修改与记录的度量相称的所分配资源集合中的所分配的资源中的至少一个。 该方法使用经修改的分配资源集来识别来自扬声器的附加语音。 指标可以包括语音识别置信度分数，处理速度，对话行为，重复请求，对确认的否定响应以及任务完成。 该方法还可以存储具有用于所修改的分配资源集合的信息的扬声器个性化简档，并且基于说话者个性化简档识别与说话者相关联的语音。

公开(公告)号：US08073693B2

公开(公告)日：2011-12-06

申请号：US12328407

申请日：2008-12-04

申请人： Andrej Ljolje ,  Alistair D. Conkie ,  Ann K. Syrdal

发明人： Andrej Ljolje ,  Alistair D. Conkie ,  Ann K. Syrdal

IPC分类号： G10L15/02

CPC分类号： G10L15/187 ,  G10L15/183 ,  G10L2015/025

摘要： Systems, computer-implemented methods, and tangible computer-readable media for generating a pronunciation model. The method includes identifying a generic model of speech composed of phonemes, identifying a family of interchangeable phonemic alternatives for a phoneme in the generic model of speech, labeling the family of interchangeable phonemic alternatives as referring to the same phoneme, and generating a pronunciation model which substitutes each family for each respective phoneme. In one aspect, the generic model of speech is a vocal tract length normalized acoustic model. Interchangeable phonemic alternatives can represent a same phoneme for different dialectal classes. An interchangeable phonemic alternative can include a string of phonemes.

摘要翻译： 系统，计算机实现的方法和用于生成发音模型的有形计算机可读介质。 该方法包括识别由音素组成的通用语音模型，在通用语音模型中识别音素的可互换音素替代品系列，将可互换音素替代品的家族标记为指相同的音素，以及生成发音模型，其中 将每个家庭的每个音素替代。 在一个方面，语音的通用模型是声道长度归一化声学模型。 可互换的音素替代品可以代表不同方言课程的相同音素。 可互换的音素替代品可以包括一串音素。

公开(公告)号：US09026444B2

公开(公告)日：2015-05-05

申请号：US12561005

申请日：2009-09-16

申请人： Andrej Ljolje ,  Diamantino Antonio Caseiro ,  Alistair D. Conkie

发明人： Andrej Ljolje ,  Diamantino Antonio Caseiro ,  Alistair D. Conkie

IPC分类号： G10L15/22 ,  G10L15/07 ,  G10L15/06

CPC分类号： G10L15/07 ,  G10L15/04 ,  G10L15/063 ,  G10L15/083 ,  G10L15/14 ,  G10L15/22 ,  G10L15/28 ,  G10L15/32

摘要： Disclosed herein are methods, systems, and computer-readable storage media for automatic speech recognition. The method includes selecting a speaker independent model, and selecting a quantity of speaker dependent models, the quantity of speaker dependent models being based on available computing resources, the selected models including the speaker independent model and the quantity of speaker dependent models. The method also includes recognizing an utterance using each of the selected models in parallel, and selecting a dominant speech model from the selected models based on recognition accuracy using the group of selected models. The system includes a processor and modules configured to control the processor to perform the method. The computer-readable storage medium includes instructions for causing a computing device to perform the steps of the method.

摘要翻译： 这里公开了用于自动语音识别的方法，系统和计算机可读存储介质。 该方法包括选择一个说话者独立模型，并选择一个说话者依赖模型的数量，说话人依赖模型的数量是基于可用的计算资源，所选择的模型包括与说话者无关的模型和说话者依赖模型的数量。 该方法还包括使用所选择的模型中的每一个并行地识别话语，并且基于使用所选择的模型的组的识别精度从所选择的模型中选择主要语言模型。 该系统包括处理器和被配置为控制处理器执行该方法的模块。 计算机可读存储介质包括用于使计算设备执行该方法的步骤的指令。

公开(公告)号：US08862470B2

公开(公告)日：2014-10-14

申请号：US13302380

申请日：2011-11-22

申请人： Andrej Ljolje ,  Alistair D. Conkie ,  Ann K. Syrdal

发明人： Andrej Ljolje ,  Alistair D. Conkie ,  Ann K. Syrdal

IPC分类号： G10L15/187 ,  G10L15/183

CPC分类号： G10L15/187 ,  G10L15/183 ,  G10L2015/025

摘要： Systems, computer-implemented methods, and tangible computer-readable media for generating a pronunciation model. The method includes identifying a generic model of speech composed of phonemes, identifying a family of interchangeable phonemic alternatives for a phoneme in the generic model of speech, labeling the family of interchangeable phonemic alternatives as referring to the same phoneme, and generating a pronunciation model which substitutes each family for each respective phoneme. In one aspect, the generic model of speech is a vocal tract length normalized acoustic model. Interchangeable phonemic alternatives can represent a same phoneme for different dialectal classes. An interchangeable phonemic alternative can include a string of phonemes.

摘要翻译： 系统，计算机实现的方法和用于生成发音模型的有形计算机可读介质。 该方法包括识别由音素组成的通用语音模型，在通用语音模型中识别音素的可互换音素替代品系列，将可互换音素替代品的家族标记为指相同的音素，以及生成发音模型，其中 将每个家庭的每个音素替代。 在一个方面，语音的通用模型是声道长度归一化声学模型。 可互换的音素替代品可以代表不同方言课程的相同音素。 可互换的音素替代品可以包括一串音素。